With the rapid development of quantum technologies in the past decades, the cryptographic security of classical communication channels is at potential risk. Quantum key distribution(QKD) systems promise a viable solution to this problem. Currently, the connection between parties in a QKD system is maintained by either optical fiber or free space links. It is known that with optical fibers hundreds of kilometer links are established. On the other hand, free space links provide more flexibility but the stability of the system is highly dependent on diverse factors such as atmospheric turbulence. For achieving the highest security in QKD systems single photon sources are ideal as the light source. However, their emission stability highly depends on the structure of the quantum emitter. Alternatively, attenuated laser pulses can be integrated to the system since they provide stable operation. In this work, using an attenuated pulsed laser we constructed free space quantum
key distribution setup based on B92 protocol. We took measurements both for 32 and 130 meter links. The results are promising to extend the range of optical links over several kilometers with further development, such as implementing a true single photon source. This work was supported by the Scientific and Technological Research Council of Türkiye (TUBITAK) with a grant number 117F495.

Recent advances in scanning tunnelling spectroscopy have enabled more direct probing of quantum phases of matter in two-dimensional materials. Some of these phases, particularly those found in bilayer graphene, are theorized to host quasiparticle excitations known as non-abelian anyons which can be utilized for topological quantum computation. This form of quantum computing takes advantage of the nonlocality of multi-particle wave functions to perform useful computations insensitive to local noise through braiding of the constituent particles. These exotic phases of matter have been theorized to exist for decades in various form factors including the Kitaev honeycomb model, Kitaev spin chain, and non-abelian fractional quantum Hall phases, but their experimental realization has proved challenging. Bilayer graphene provides a more realistic and equally powerful approach to realizing and probing non-abelian anyons. We will provide a brief overview of various physical realizations of non-abelian anyons focusing on bilayer graphene and will cover the fundamentals of topological quantum computation.

The past decade has observed an interplay between two of the most exciting fields of science: quantum gravity and quantum information science. This presentation is dedicated to providing a review of its background, development, and recent progress. Following the introduction of Hawking radiation and its implication of information loss, many theories have been proposed in order to resolve this issue; however, they all come with several drawbacks. Recently, a bold conjecture relates a completely separate field to this concept: the idea that the principle of computational complexity can be applied to the description of black holes under AdS/CFT correspondence. At the center of this conjecture is the idea of quantum computational complexity, or how many gates or operations are needed to reach a given state. Maldacena’s theory of AdS/CFT correspondence connects Anti-de Sitter bulk space with the conformal field theory over the interior boundary geometry, by which the information inside of a black hole is holographically projected onto its horizon. Building on this theory, recent conjectures connect the volume of a black hole to the quantum computational complexity over its horizon. This idea, known as CV duality, provides a solid approach to understanding the growth of a black hole’s volume via analysis of certain quantum circuits. One such “toy model” circuit is a random quantum circuit. Such a circuit is fast-scrambling and is characterized by a complexity with unstable decreases and stable increases. Bounding the quantum complexity of this random model has been the subject of much investigation in recent years, with several approaches proving that this complexity grows linearly in exponential time, a pattern which mirrors the volumetric growth of a black hole. As such, researching certain circuit models in quantum computing can yield surprising results in such a seemingly disconnected field as black hole physics.

Breast cancer is the second most common cancer among women in the United States, and effective diagnosis can improve patient outcomes. Machine learning algorithms can help physicians with classifying of tumors as benign or malignant. Quantum neural networks have been shown to hold quantum advantage over classical neural networks for certain datasets. Prior work on the CBIS-DDSM breast cancer dataset has achieved at best 67% accuracy, and it is unknown whether quantum advantage can help for classifying this dataset. We train and evaluate a hybrid classical-quantum transfer learning model on CBIS-DDSM patches and present preliminary results to suggest that quantum advantage is not helpful for this dataset.

Quantum computation is a burgeoning new field that exists at the intersection
of computer science, physics, and engineering. Classical computers, which are
limited by classical physics principles, use binary bits to represent data as ones
or zeros. Logic gates are then used to manipulate and process these bits to perform sequential computations. On the other hand, quantum computers leverage
the principles of quantum mechanics via qubits (quantum bits) which use superposition and entanglement to exist in multiple states at the same time. This
allows quantum computers to perform multiple computations simultaneously,
increasing computational efficiency.

Despite this enticing concept, contemporary quantum computers still face
significant challenges in implementing useful quantum algorithms. Today’s
quantum computers have an upper limit of a few hundred qubits. These machines are known as noisy intermediate-scale quantum (NISQ) machines and
limit the feasibility of many quantum algorithms.

One way to overcome these hardware limitations is to use a Variational
Quantum Algorithm, also known as a VQA. This allows us to perform hybrid
quantum-classical computations with fewer qubits. VQAs employ a classical
optimizer to train a parameterized quantum circuit. They have a plethora of
potential applications, including modeling quantum many-body systems, training quantum neural networks, and solving linear systems of equations. In this
paper, we will provide an overview of VQAs and detail a specific application of
them for solving linear systems.

The term Noisy Intermediate-Scale Quantum (NISQ), describing the regime of quantum devices we have access to today, self-explains one of the fundamental hurdles for fault-tolerant quantum computing—quantum computers are susceptible to noise internal and external to the device. To address this hurdle, the field of quantum error correction (QEC) was erected, expanding on the well-developed field of classical error correction. Today, companies, such as Google, have put forth a massive effort towards incorporating error correcting codes into their near-term devices, namely the Surface Code. Although the Surface Code architecture has a good threshold figure, it falls short in the number of logical qubits it is able to encode as well as the distance of the code; thus, it limits its potential for useful, fault-tolerant quantum computation. In this talk, I will give a pedagogical overview of Quantum Low-Density Parity Check (QLDPC) codes, a class of codes that boast asymptotically-good encoding rate and code distance, as well as discuss their various constructions. I will weigh the strengths and weaknesses of these codes and discuss their feasibility in near and long-term quantum devices. Finally, I will discuss the decoding problem—the algorithmic problem of how errors are actually corrected on a quantum device—with respect to QLDPC codes.

The field of quantum computing, albeit highly interdisciplinary, demands a steep learning curve. Participants are often exposed to years of upper-division university coursework before contributions are made. Familiarizing a broad audience with fundamental principles of quantum computing has thus been a major challenge within the community. We present an approach to teach the concepts of superposition and entanglement through gamification. Using the well-known card game Blackjack (or 21), we adapt the set-up of the game to incorporate the principles but maintain the rules of the original game adequately to allow for immediate play. In this presentation, we discuss the game design and implementation. A study on a wide audience is conducted, and the success of the game is assessed. The results of our study are discussed, and learnings for future iterations are unveiled.

Quantum algorithms are driving investment in the quantum industry with the promise of revolutionizing and disrupting various industries, but the limitations of noisy intermediate scale quantum (NISQ) devices have hindered their potential. Variational quantum algorithms (VQAs) have been developed as quantum-classical hybrid algorithms that rely on classical optimization and are considered leading candidates for delivering quantum applications in the near term. However, their real-world potential and societal impact have not been systematically investigated, and a comprehensive technical analysis of each algorithm's performance at scale and analysis of market forces determining their use are lacking.

This project aims to answer two questions:
1) Can VQAs be ranked by immediate social impact and compatibility with intermediate scale noisy devices?
2) What is the likely social impact of achieving quantum advantage with VQAs, and how can regulatory frameworks minimize potential harm and promote positive applications?

Over 60 VQAs exist, but a systematic analysis of their performance at scale and market forces has not been undertaken. This presentation will discuss one of these VQAs and intends to provide informed recommendations for ethical use of such algorithms.

Autonomous cars, such as those that Tesla Inc. builds, use complex deep neural networks to make instantaneous decisions based on real-time data from sensors on the vehicle. One major example of an autopilot mode feature is controlling the steering angle of the vehicle relative to the road. To build better self-driving models, we either need billions of miles of driving data or more complex models that train faster on lesser data. Conventional deep neural networks like the Convolutional Neural Network (CNN) are excellent at predicting steering angles by analyzing image data as input. This project entails constructing a hybrid neural network that combines classical and “quantum” layers in order to predict the steering angle for a car based on video data. Once a functional model is built, we will build a fully classical CNN and use it to compare against our hybrid CNN. The premise behind exploring this project lies in using quantum transfer learning methods similar to Variational Quantum Eigensolvers. We have a quantum circuit and an optimizer which optimizes parameters for a set of iterations till we find a desired value using quantum circuits. We will be using the layers in a deep neural network and transferring the weights as parameters to the quantum circuits. The quantum circuits will augment the classical neural network and help find the optimum steering angle based on images from a car’s front camera. We shall build a deep CNN with several convolutional layers, and the following layers will be the variational quantum circuits. We will evaluate the models’ performances based on the prediction of the steering angle and the time it takes for the neural networks to be trained.

Variational Quantum Circuits (VQCs) are one way to implement quantum machine learning on current hardware. VQCs are convenient as they behave similarly to classical neural networks, however VQCs have one main flaw: the circuit ansatz. At a high level, the circuit ansatz constrains the VQC to search in a small subspace of all possible solutions; it's generally impossible to know if this subspace contains the optimal solution. Thus, due to the circuit ansatz, it's difficult to make any statements about the performance of the VQC relative to the full solution space. In order to fix this issue, we developed a Genetic Algorithm (GA) to optimize the circuit ansatz and, therefore, better search the full solution space. Using this GA tool, a circuit ansatz can be optimized for any problem. To demonstrate the power of this GA, we've applied the GA to a quantum autoencoder VQC for high energy physics anomaly detection.

Implementing large-scale superconducting quantum computers that are also fault tolerant requires the integration of qubits of numbers in the order of magnitude of millions, ones that have to have very low error rates, i.e. long lifetimes. One of the three most common methods to increase the lifetime of a qubit, error protection, is mainly realized through different qubit architectures such as with 0 − π qubit. However, the fabrication and control of these qubits are particularly difficult due to the complex circuitry usually involving multiple flux loops. Easier to fabricate and control mainly due to having a single flux loop, the Superconducting Nonlinear Asymmetric Inductive eLement (SNAIL) is a viable candidate for a protected qubit. However, the internal modes, including the degeneracy of different modes, the probabilities of transition between individual energy levels, and how transitions change due to the initial parameters of the circuit for the SNAIL (otherwise known as a GFQ with N = 3 Josephson junctions) remain unexplored. This paper aims to analyze energy level degeneracies and patterns in matrix elements in order to find circuit parameters for SNAIL such that the circuit is in a protected subspace and to discuss the accuracy of the current lumped-abstraction model for SNAIL. It was thus found that the GFQ has energy modes with varying degeneracies, labeled as highly degenerate (0-0.25 GHz), degenerate (0.25-2.5 GHz), slightly degenerate (2.5-7.5 GHz), and non-degenerate (above 7.5 GHz), the latter being the only ng insensitive regime. Moreover, all of the degenerate regimes show similar patterns to the protomon protected qubit when ng = 0. When the ratio EJ /EC is set at a large value to avoid sensitivity to ng, we see that the non-degenerate regime shows a similar pattern to the 0 − π protected qubit at very large ng values (> 10).

Designing practical quantum architecture has proven to be a challenging yet essential task for the implementation of quantum computing. After the initial theoretical framework for quantum computing was established, the race began to develop technologies that could apply this theoretical framework to solve problems. Three major competing technologies emerged; superconducting circuits, trapped ion qubits, and neutral atom qubits. In this presentation, we will explore the nuances of each of these technologies, how the architectural design of quantum computers constructs complex arrays of qubits in different ways, and how the different technologies compare with each other. We will provide an intuitive introduction to contemporary quantum engineering challenges within these architectures, such as enhancing gate fidelity, developing practical quantum error correction codes, and making quantum memory management more efficient. We will further explore ways to alleviate bottlenecks in each quantum technology, providing a foundation for larger, more scalable, quantum systems. Through this presentation, we aim to create a comprehensive overview of how modern quantum architecture implements the theoretical breakthroughs in quantum information science, and we provide an outlook into the future as to how these architectures might change over time and shape the quantum tomorrow.

Much of the advantage of quantum computing comes from the fact that the system begins in superposition, and the whatever algorithm is being performed can eliminate incorrect answers from the superposition through interference. As such, this initial superposition constitutes a search space from which the algorithm extracts an answer. However, via the standard way of creating an equal superposition (Hadamard gates), for a true search space of size N, the algorithm will have a search space of 2^k >= N for some integer k. This creates an auxiliary search space in which it is known an answer does not exist and which will increase computing time. I am presenting an algorithm which generates a circuit that converts the |0> state into an equal superposition of exactly N states for any arbitrary N, eliminating the auxiliary search space. I am also presenting an application of this algorithm as a modification to Grover's Algorithm to solve a k-Coloring Graph Problem.

Quantum Optics can give insight into quantum communication by utilizing light, more specifically photons, as quantum information. Quantum optics experiments also provide a hands-on way of learning quantum mechanics.
Throughout the talk, we will explore the wave/particle duality of light. This talk will begin with an introduction to quantum optics as well as a virtual laboratory software capable of simulating quantum optics experiments. We will discuss the wave-like behavior of light by analyzing an example of interference. Then, we will discuss the particle-like behavior by analyzing an example of anticorrelation.

Quantum computing offers enhanced performance capabilities and more efficient solutions to computational problems; however, the fragility of qubits is a stark limitation on the much anticipated potential of this alternate form of computing. Error correction is a means through which disrupted signals subject to external electrical interference can be adjusted to preserve the original message. Quantum error correction achieves this through embedding the original vector space into an expanded Hilbert space through quantum entanglement. This talk discusses the essential nature of error correction in quantum computing, and later moves into the differences between classical and quantum error correction. I will also explain three different types of quantum error correction code: the correction of single qubit bit flips, the correction of single qubit phase flips, and Shor’s 9 qubit quantum error correction algorithm.

The recent biotechnological progress has allowed life scientists and physicians to access an unprecedented, massive amount of data at all levels (molecular, supramolecular, cellular and so on) of biological complexity. So far, mostly classical computational efforts have been dedicated to the simulation, prediction or de novo design of biomolecules, in order to improve the understanding of their function or to develop novel therapeutics. At a higher level of complexity, the progress of omics disciplines (genomics, transcriptomics, proteomics and metabolomics) has prompted researchers to develop informatics means to describe and annotate new biomolecules identified with a resolution down to the single cell, but also with a high-throughput speed. Machine learning approaches have been implemented to both the modelling studies and the handling of biomedical data. Quantum computing (QC) approaches hold the promise to resolve, speed up or refine the analysis of a wide range of these computational problems. Here, we review and comment on recently developed QC algorithms for biocomputing, with a particular focus on multi-scale modelling and genomic analyses. Indeed, differently from other computational approaches such as protein structure prediction, these problems have been shown to be adequately mapped onto quantum architectures, the main limit for their immediate use being the number of qubits and decoherence effects in the available quantum machines. Possible advantages over the classical counterparts are highlighted, along with a description of some hybrid classical/quantum approaches, which could be the closest to be realistically applied in biocomputation.

This presentation unveils ""Quangry Cats,"" an innovative educational game designed to kindle interest and understanding in the often complex, abstract field of quantum information science. Leveraging gamified learning, Quangry Cats aims to decode quantum mechanics principles, making them accessible and engaging to both the younger generation and the science-interested general public alike.
""Quangry Cats"" is based on the hit mobile game Angry Birds with added quantum principles. Players navigate through levels, overcoming challenges and puzzles that illustrate quantum behaviors. By interactively manipulating both classical and “quangry” cats, learners witness first-hand the unusual phenomena of the quantum world.
This presentation will discuss the game's conception, design, and pedagogical strategies. The session promises insights into the potential of playful, interactive platforms in quantum information science education, hoping to inspire a new generation of quantum enthusiasts.
You can play our game online at: https://alexander-yue.itch.io/quangry-cats

Electrons are indivisible elementary particles, yet paradoxically a collection of them can act as a fraction of a single electron, exhibiting exotic and useful properties. One such collective excitation, known as a topological Majorana mode, is naturally stable against perturbations, such as unwanted local noise, and can thereby robustly store quantum information. As such, Majorana modes serve as the basic primitive of topological quantum computing, providing resilience to errors. However, their demonstration on quantum hardware has remained elusive. Here, we demonstrate a verifiable identification and braiding of topological Majorana modes using a superconducting quantum processor as a quantum simulator. By simulating fermions on a one-dimensional lattice subject to a periodic drive, we confirm the existence of Majorana modes localized at the edges, and distinguish them from other trivial modes. To simulate a basic logical operation of topological quantum computing known as braiding, we propose a non-adiabatic technique, whose implementation reveals correct braiding statistics in our experiments. This work could further be used to study topological models of matter using circuit-based simulations, and shows that long-sought quantum phenomena can be realized by anyone in cloud-run quantum simulations, whereby accelerating fundamental discoveries in quantum science and technology.